BCA 6th Sem -Data Science and Machine Learning UNIT-II MCQ

 

BCA 6th Sem -Data Science and Machine Learning UNIT-II MCQ

• UNIT-I 

Introduction to Data Science :                                     -Evolution of Data Science    - Data Science  Roles             - Stages in a Data Science Project                                          -Applications of Data Science in various fields        -Data Security Issues
   .
 Unit-1 MCQ's

UNIT-II 

Data Collection and Data Pre-Processing :                      -DataCollection Strategies, -Data Pre-Processing Overview                                   -Data Cleaning                       -Data Integration and Transformation                           -Data Reduction  

Unit-2 MCQ's

UNIT-III 

Exploratory Data Analytics :                               - Descriptive Statistics - Mean, StandardDeviation,    -Skewness and Kurtosis         -Box Plots                                – Pivot Table,                          -Correlation Statistics,           - ANOVA,                                            

Unit-3 MCQ's

UNIT-IV 

-Idea of Machines learning from data                                  -Classification of problem – Regression and Classification                            -Supervised and Unsupervised learning.                                  

Unit-4 MCQ's

UNIT-V                 

Neural Networks : 

-History, 

-Artificial and biological neural networks 

-Artificial intelligence and neural networks -

-Biological neurons              -Models of single neurons   -Different neural network models Neural Networks 

Unit-5 MCQ's

Data Science and Machine Learning 

1. What is the primary goal of data collection in a Data Science project?
   A) To analyze data
   B) To gather relevant data for analysis
   C) To visualize data
   D) To clean data
   Answer: B) To gather relevant data for analysis

2. Which of the following is a common method for data collection?
   A) Surveys
   B) Web scraping
   C) APIs
   D) All of the above
   Answer: D) All of the above

3. What is the purpose of data pre-processing?
   A) To analyze data
   B) To prepare raw data for analysis by cleaning and transforming it
   C) To visualize data
   D) To collect more data
   Answer: B) To prepare raw data for analysis by cleaning and transforming it

4. Which of the following is NOT a step in data pre-processing?
   A) Data cleaning
   B) Data transformation
   C) Data visualization
   D) Data integration
   Answer: C) Data visualization

5. What is data cleaning primarily concerned with?
   A) Removing duplicates and correcting errors in the dataset
   B) Collecting new data
   C) Analyzing data
   D) Visualizing data
   Answer: A) Removing duplicates and correcting errors in the dataset

6. Which technique is commonly used to handle missing data?
   A) Deletion
   B) Imputation
   C) Interpolation
   D) All of the above
   Answer: D) All of the above

7. What is the purpose of normalization in data pre-processing?
   A) To reduce the dimensionality of the data
   B) To scale the data to a specific range
   C) To remove outliers
   D) To visualize data
   Answer: B) To scale the data to a specific range

8. Which of the following is a common method for data transformation?
   A) Log transformation
   B) Min-max scaling
   C) Standardization
   D) All of the above
   Answer: D) All of the above

9. What is the significance of feature selection in data pre-processing?
   A) To reduce the number of input variables
   B) To increase the complexity of the model
   C) To visualize data
   D) To clean the data
   Answer: A) To reduce the number of input variables

10. Which of the following is a common challenge in data collection?
    A) Data quality issues
    B) Data privacy concerns
    C) Data integration from multiple sources
    D) All of the above
    Answer: D) All of the above

11. What is the purpose of data integration in the pre-processing stage?
    A) To combine data from different sources into a unified dataset
    B) To clean the data
    C) To visualize data
    D) To analyze data
    Answer: A) To combine data from different sources into a unified dataset

12. Which of the following is a common tool used for data collection?
    A) SQL
    B) Python
    C) Excel
    D) All of the above
    Answer: D) All of the above

13. What is the role of exploratory data analysis (EDA) in data pre-processing?
    A) To visualize data
    B) To summarize the main characteristics of the data
    C) To clean the data
    D) To collect more data
    Answer: B) To summarize the main characteristics of the data

14. Which of the following is a method for detecting outliers in a dataset?
    A) Z-score analysis
    B) IQR method
    C) Visualization techniques (e.g., box plots)
    D) All of the above
    Answer: D) All of the above

15. What is the significance of data types in data pre-processing?
    A) They determine how data can be analyzed and processed
    B) They have no impact on data analysis
    C) They are only relevant for data visualization
    D) They are only relevant for data collection
    Answer: A) They determine how data can be analyzed and processed

16. Which of the following is a common data format used for data collection?
    A) CSV
    B) JSON
    C) XML
    D) All of the above
    Answer: D) All of the above

17. What is the purpose of data encoding in data pre-processing?
    A) To convert categorical data into numerical format
    B) To clean the data
    C) To visualize data
    D) To collect more data
    Answer: A) To convert categorical data into numerical format

18. Which of the following is a common technique for handling categorical variables?
    A) One-hot encoding
    B) Label encoding
    C) Both A and B
    D) None of the above
    Answer: C) Both A and B

19. What is the main goal of data normalization?
    A) To ensure that different features contribute equally to the analysis
    B) To increase the size of the dataset
    C) To remove irrelevant features
    D) To visualize data
    Answer: A) To ensure that different features contribute equally to the analysis

20. Which of the following is a potential issue when collecting data from online sources?
    A) Data may be outdated
    B) Data may be biased
    C) Data may be incomplete
    D) All of the above
    Answer: D) All of the above

21. What is the purpose of data sampling in data collection?
    A) To analyze the entire dataset
    B) To select a representative subset of data for analysis
    C) To visualize data
    D) To clean the data
    Answer: B) To select a representative subset of data for analysis

22. Which of the following is a method for ensuring data quality during collection?
    A) Implementing validation rules
    B) Regularly updating data sources
    C) Training data collectors
    D) All of the above
    Answer: D) All of the above

23. What is the significance of data provenance in data collection?
    A) It tracks the origin and history of the data
    B) It ensures data is collected quickly
    C) It visualizes data
    D) It cleans the data
    Answer: A) It tracks the origin and history of the data

24. Which of the following is a common challenge in data pre-processing?
    A) Handling missing values
    B) Ensuring data consistency
    C) Dealing with noise in the data
    D) All of the above
    Answer: D) All of the above

25. What is the role of data visualization in the data pre-processing phase?
    A) To clean the data
    B) To provide insights and identify patterns
    C) To collect more data
    D) To analyze data
    Answer: B) To provide insights and identify patterns

26. What is the primary purpose of a data collection strategy?
    A) To analyze data
    B) To define how data will be gathered and managed
    C) To visualize data
    D) To clean data
    Answer: B) To define how data will be gathered and managed

27. Which of the following is a qualitative data collection method?
    A) Surveys with open-ended questions
    B) Structured interviews
    C) Observations
    D) All of the above
    Answer: D) All of the above

28. What is a common quantitative data collection method?
    A) Focus groups
    B) Online surveys with closed-ended questions
    C) Case studies
    D) Ethnographic studies
    Answer: B) Online surveys with closed-ended questions

29. Which data collection strategy involves gathering data from existing sources?
    A) Primary data collection
    B) Secondary data collection
    C) Tertiary data collection
    D) Qualitative data collection
    Answer: B) Secondary data collection

30. What is the main advantage of using surveys for data collection?
    A) They are time-consuming
    B) They can reach a large audience quickly
    C) They provide in-depth qualitative data
    D) They are expensive
    Answer: B) They can reach a large audience quickly

31. Which of the following is a disadvantage of using interviews for data collection?
    A) They provide rich qualitative data
    B) They can be time-consuming and costly
    C) They allow for in-depth exploration of topics
    D) They can be easily standardized
    Answer: B) They can be time-consuming and costly

32. What is the purpose of using focus groups in data collection?
    A) To gather quantitative data
    B) To explore participants' attitudes and perceptions in depth
    C) To conduct large-scale surveys
    D) To analyze existing data
    Answer: B) To explore participants' attitudes and perceptions in depth

33. Which data collection method is best suited for understanding user behavior on a website?
    A) Surveys
    B) Web analytics
    C) Interviews
    D) Focus groups
    Answer: B) Web analytics

34. What is a key consideration when designing a data collection strategy?
    A) The cost of data collection
    B) The target population
    C) The type of data needed
    D) All of the above
    Answer: D) All of the above

35. Which of the following is a method for collecting observational data?
    A) Surveys
    B) Experiments
    C) Field studies
    D) All of the above
    Answer: D) All of the above

36. What is the main advantage of using experiments for data collection?
    A) They are inexpensive
    B) They allow for control over variables
    C) They provide qualitative insights
    D) They are easy to conduct
    Answer: B) They allow for control over variables

37. Which of the following is a common tool for online data collection?
    A) Google Forms
    B) SurveyMonkey
    C) Qualtrics
    D) All of the above
    Answer: D) All of the above

38. What is the purpose of using a sampling strategy in data collection?
    A) To analyze the entire population
    B) To select a representative subset of the population
    C) To avoid data collection
    D) To visualize data
    Answer: B) To select a representative subset of the population

39. Which sampling method involves selecting participants based on specific characteristics?
    A) Random sampling
    B) Stratified sampling
    C) Convenience sampling
    D) Systematic sampling
    Answer: B) Stratified sampling

40. What is a disadvantage of convenience sampling?
    A) It is time-consuming
    B) It may lead to biased results
    C) It is difficult to implement
    D) It is expensive
    Answer: B) It may lead to biased results

41. Which of the following is a method for ensuring data quality during collection?
    A) Implementing validation checks
    B) Training data collectors
    C) Regularly reviewing data collection processes
    D) All of the above
    Answer: D) All of the above

42. What is the main goal of longitudinal studies in data collection?
    A) To collect data at a single point in time
    B) To gather data over an extended period
    C) To analyze existing data
    D) To conduct experiments
    Answer: B) To gather data over an extended period

43. Which of the following is a common challenge in data collection?
    A) Ensuring participant confidentiality
    B) Data entry errors
    C) Non-response bias
    D) All of the above
    Answer: D) All of the above

44. What is the purpose of pilot testing a data collection instrument?
    A) To finalize the data collection strategy
    B) To identify potential issues and improve the instrument before full deployment
    C) To analyze the data collected
    D) To visualize the data
    Answer: B) To identify potential issues and improve the instrument before full deployment

45. Which of the following is a key factor in determining the sample size for a study?
    A) The budget available for data collection
    B) The desired level of precision and confidence
    C) The time available for data collection
    D) All of the above
    Answer: D) All of the above

46. What is the main advantage of using mixed methods in data collection?
    A) It simplifies the analysis process
    B) It combines the strengths of both qualitative and quantitative approaches
    C) It reduces the time needed for data collection
    D) It eliminates the need for sampling
    Answer: B) It combines the strengths of both qualitative and quantitative approaches

47. Which of the following is a common ethical consideration in data collection?
    A) Informed consent from participants
    B) Data ownership
    C) Anonymity and confidentiality
    D) All of the above
    Answer: D) All of the above

48. What is the purpose of using a control group in experimental data collection?
    A) To provide a baseline for comparison
    B) To increase the sample size
    C) To ensure data quality
    D) To collect qualitative data
    Answer: A) To provide a baseline for comparison

49. Which of the following is a potential source of bias in data collection?
    A) Non-random sampling
    B) Leading questions in surveys
    C) Participant self-selection
    D) All of the above
    Answer: D) All of the above

50. What is the significance of data triangulation in data collection?
    A) It enhances the validity and reliability of the data
    B) It simplifies the data analysis process
    C) It reduces the cost of data collection
    D) It eliminates the need for sampling
    Answer: A) It enhances the validity and reliability of the data

51. What is the primary goal of data cleaning?
    A) To analyze data
    B) To prepare data for analysis by correcting errors and inconsistencies
    C) To visualize data
    D) To collect more data
    Answer: B) To prepare data for analysis by correcting errors and inconsistencies

52. Which of the following is a common issue that data cleaning addresses?
    A) Missing values
    B) Duplicate records
    C) Outliers
    D) All of the above
    Answer: D) All of the above

53. What is a common method for handling missing data?
    A) Deletion
    B) Imputation
    C) Using a placeholder value
    D) All of the above
    Answer: D) All of the above

54. Which technique is used to identify and remove duplicate records in a dataset?
    A) Data normalization
    B) Data deduplication
    C) Data transformation
    D) Data integration
    Answer: B) Data deduplication

55. What is the purpose of outlier detection in data cleaning?
    A) To remove irrelevant data
    B) To identify and handle extreme values that may skew analysis
    C) To visualize data
    D) To collect more data
    Answer: B) To identify and handle extreme values that may skew analysis

56. Which of the following is a method for detecting outliers?
    A) Z-score analysis
    B) IQR method
    C) Visualization techniques (e.g., box plots)
    D) All of the above
    Answer: D) All of the above

57. What is the significance of data validation in the data cleaning process?
    A) To ensure data is accurate and meets predefined criteria
    B) To visualize data
    C) To collect more data
    D) To analyze data
    Answer: A) To ensure data is accurate and meets predefined criteria

58. Which of the following is a common technique for data transformation during cleaning?
    A) Normalization
    B) Standardization
    C) Log transformation
    D) All of the above
    Answer: D) All of the above

59. What is the purpose of data normalization?
    A) To scale data to a specific range
    B) To remove duplicates
    C) To handle missing values
    D) To visualize data
    Answer: A) To scale data to a specific range

60. Which of the following is a common challenge in data cleaning?
    A) Handling large volumes of data
    B) Ensuring data consistency
    C) Dealing with various data formats
    D) All of the above
    Answer: D) All of the above

61. What is the role of data profiling in the data cleaning process?
    A) To analyze the structure and content of the data
    B) To visualize data
    C) To collect more data
    D) To remove duplicates
    Answer: A) To analyze the structure and content of the data

62. Which of the following is a method for handling categorical variables during data cleaning?
    A) One-hot encoding
    B) Label encoding
    C) Both A and B
    D) None of the above
    Answer: C) Both A and B

63. What is the purpose of data type conversion in data cleaning?
    A) To change the format of data to ensure it is appropriate for analysis
    B) To remove duplicates
    C) To handle missing values
    D) To visualize data
    Answer: A) To change the format of data to ensure it is appropriate for analysis

64. Which of the following is a common tool used for data cleaning?
    A) Excel
    B) Python (with libraries like Pandas)
    C) R
    D) All of the above
    Answer: D) All of the above

65. What is the significance of maintaining data integrity during the cleaning process?
    A) To ensure that data remains accurate and reliable
    B) To visualize data
    C) To collect more data
    D) To analyze data
    Answer: A) To ensure that data remains accurate and reliable

66. Which of the following is a potential consequence of poor data cleaning?
    A) Inaccurate analysis results
    B) Increased data processing time
    C) Misleading insights
    D) All of the above
    Answer: D) All of the above

67. What is the purpose of using regular expressions in data cleaning?
    A) To visualize data
    B) To search for and manipulate text patterns in data
    C) To handle missing values
    D) To remove duplicates
    Answer: B) To search for and manipulate text patterns in data

68. Which of the following is a common practice for ensuring data consistency?
    A) Standardizing data formats
    B) Implementing validation rules
    C) Regularly reviewing data
    D) All of the above
    Answer: D) All of the above

69. What is the role of data enrichment in the data cleaning process?
    A) To add additional information to existing data
    B) To remove irrelevant data
    C) To visualize data
    D) To analyze data
    Answer: A) To add additional information to existing data

70. Which of the following is a method for detecting data entry errors?
    A) Cross-referencing with external sources
    B) Using automated validation checks
    C) Manual review of data
    D) All of the above
    Answer: D) All of the above

71. What is the purpose of data deduplication?
    A) To enhance data quality by removing duplicate entries
    B) To visualize data
    C) To collect more data
    D) To analyze data
    Answer: A) To enhance data quality by removing duplicate entries

72. Which of the following can be a source of data quality issues?
    A) Human error during data entry
    B) Inconsistent data formats
    C) Lack of data governance
    D) All of the above
    Answer: D) All of the above

73. What is the significance of documenting the data cleaning process?
    A) To ensure transparency and reproducibility
    B) To visualize data
    C) To collect more data
    D) To analyze data
    Answer: A) To ensure transparency and reproducibility

74. Which of the following is a common approach to handle outliers?
    A) Removing them from the dataset
    B) Transforming them
    C) Keeping them if they are valid
    D) All of the above
    Answer: D) All of the above

75. What is the purpose of using data dictionaries in data cleaning?
    A) To provide metadata about the data
    B) To visualize data
    C) To collect more data
    D) To analyze data
    Answer: A) To provide metadata about the data

76. Which of the following is a common challenge when cleaning unstructured data?
    A) Lack of predefined formats
    B) High volume of data
    C) Difficulty in extracting meaningful information
    D) All of the above
    Answer: D) All of the above

77. What is the role of data governance in the data cleaning process?
    A) To establish policies and standards for data quality
    B) To visualize data
    C) To collect more data
    D) To analyze data
    Answer: A) To establish policies and standards for data quality

78. Which of the following is a technique for handling inconsistent data?
    A) Standardization
    B) Normalization
    C) Data transformation
    D) All of the above
    Answer: D) All of the above

79. What is the significance of using automated tools in data cleaning?
    A) To increase efficiency and reduce human error
    B) To visualize data
    C) To collect more data
    D) To analyze data
    Answer: A) To increase efficiency and reduce human error

80. Which of the following is a potential risk of not cleaning data properly?
    A) Misleading conclusions
    B) Increased operational costs
    C) Damage to reputation
    D) All of the above
    Answer: D) All of the above

81. What is the primary goal of data integration?
    A) To analyze data
    B) To combine data from different sources into a unified view
    C) To visualize data
    D) To clean data
    Answer: B) To combine data from different sources into a unified view

82. Which of the following is a common method for data integration?
    A) ETL (Extract, Transform, Load)
    B) Data warehousing
    C) APIs (Application Programming Interfaces)
    D) All of the above
    Answer: D) All of the above

83. What does ETL stand for in the context of data integration?
    A) Extract, Transform, Load
    B) Evaluate, Transform, Load
    C) Extract, Transfer, Load
    D) Evaluate, Transfer, Load
    Answer: A) Extract, Transform, Load

84. What is the purpose of data transformation?
    A) To clean data
    B) To convert data into a suitable format for analysis
    C) To visualize data
    D) To collect more data
    Answer: B) To convert data into a suitable format for analysis

85. Which of the following is a common data transformation technique?
    A) Normalization
    B) Aggregation
    C) Encoding categorical variables
    D) All of the above
    Answer: D) All of the above

86. What is the significance of data normalization?
    A) To scale data to a specific range
    B) To remove duplicates
    C) To handle missing values
    D) To visualize data
    Answer: A) To scale data to a specific range

87. Which of the following is a method for reducing the dimensionality of data?
    A) Principal Component Analysis (PCA)
    B) Data aggregation
    C) Data filtering
    D) All of the above
    Answer: A) Principal Component Analysis (PCA)

88. What is the purpose of data reduction?
    A) To decrease the volume of data while maintaining its integrity
    B) To increase the volume of data
    C) To visualize data
    D) To collect more data
    Answer: A) To decrease the volume of data while maintaining its integrity

89. Which of the following is a common challenge in data integration?
    A) Data inconsistency
    B) Data redundancy
    C) Different data formats
    D) All of the above
    Answer: D) All of the above

90. What is the role of data warehousing in data integration?
    A) To store integrated data from multiple sources
    B) To visualize data
    C) To clean data
    D) To collect more data
    Answer: A) To store integrated data from multiple sources

91. Which of the following is a technique for aggregating data?
    A) Summarizing data by groups
    B) Calculating averages
    C) Counting occurrences
    D) All of the above
    Answer: D) All of the above

92. What is the purpose of data filtering in data transformation?
    A) To remove irrelevant data
    B) To reduce data volume
    C) To enhance data quality
    D) All of the above
    Answer: D) All of the above

93. Which of the following is a common method for handling categorical data during transformation?
    A) One-hot encoding
    B) Label encoding
    C) Both A and B
    D) None of the above
    Answer: C) Both A and B

94. What is the significance of data lineage in data integration?
    A) It tracks the origin and flow of data through the integration process
    B) It ensures data quality
    C) It visualizes data
    D) It collects more data
    Answer: A) It tracks the origin and flow of data through the integration process

95. Which of the following is a potential risk of poor data integration?
    A) Inaccurate analysis results
    B) Increased operational costs
    C) Data inconsistency
    D) All of the above
    Answer: D) All of the above

96. What is the purpose of data transformation in the ETL process?
    A) To extract data from sources
    B) To load data into the target system
    C) To convert data into a suitable format for analysis
    D) To visualize data
    Answer: C) To convert data into a suitable format for analysis

97. Which of the following is a common tool used for data integration?
    A) Apache NiFi
    B) Talend
    C) Informatica
    D) All of the above
    Answer: D) All of the above

98. What is the role of metadata in data integration?
    A) To provide information about the data
    B) To visualize data
    C) To clean data
    D) To collect more data
    Answer: A ) To provide information about the data

99. Which of the following is a common challenge in data transformation?
    A) Ensuring data quality
    B) Handling large volumes of data
    C) Maintaining data integrity
    D) All of the above
    Answer: D) All of the above

100. What is the significance of data quality assessment in data integration?
     A) To evaluate the accuracy and reliability of data
     B) To visualize data
     C) To collect more data
     D) To analyze data
     Answer: A) To evaluate the accuracy and reliability of data

101. Which of the following techniques can be used for data reduction?
     A) Sampling
     B) Dimensionality reduction
     C) Data compression
     D) All of the above
     Answer: D) All of the above

102. What is the purpose of data sampling in data reduction?
     A) To select a representative subset of data for analysis
     B) To visualize data
     C) To clean data
     D) To collect more data
     Answer: A) To select a representative subset of data for analysis

103. Which of the following is a method for ensuring data consistency during integration?
     A) Data validation
     B) Standardization of formats
     C) Implementing data governance policies
     D) All of the above
     Answer: D) All of the above

104. What is the role of data transformation in preparing data for machine learning?
     A) To convert data into a format suitable for algorithms
     B) To visualize data
     C) To collect more data
     D) To analyze data
     Answer: A) To convert data into a format suitable for algorithms

105. Which of the following is a common outcome of effective data integration?
     A) Improved decision-making
     B) Enhanced data quality
     C) Streamlined operations
     D) All of the above
     Answer: D) All of the above 106. What is the significance of data reconciliation in data integration?
     A) To ensure that data from different sources matches and is accurate
     B) To visualize data
     C) To collect more data
     D) To analyze data
     Answer: A) To ensure that data from different sources matches and is accurate

106. Which of the following is a common challenge in data reduction?
     A) Loss of important information
     B) Maintaining data integrity
     C) Ensuring representativeness of the sample
     D) All of the above
     Answer: D) All of the above

107. What is the purpose of data aggregation in data transformation?
     A) To summarize data for easier analysis
     B) To visualize data
     C) To collect more data
     D) To clean data
     Answer: A) To summarize data for easier analysis

108. Which of the following techniques is used for dimensionality reduction?
     A) Feature selection
     B) PCA (Principal Component Analysis)
     C) t-SNE (t-distributed Stochastic Neighbor Embedding)
     D) All of the above
     Answer: D) All of the above

109. What is the role of data mapping in data integration?
     A) To define how data from one source corresponds to data in another
     B) To visualize data
     C) To clean data
     D) To collect more data
     Answer: A) To define how data from one source corresponds to data in another

110. Which of the following is a benefit of using data lakes for integration?
     A) Flexibility in storing various data types
     B) Cost-effectiveness
     C) Scalability
     D) All of the above
     Answer: D) All of the above

111. What is the significance of data profiling in the context of data integration?
     A) To assess the quality and structure of data before integration
     B) To visualize data
     C) To collect more data
     D) To analyze data
     Answer: A) To assess the quality and structure of data before integration

112. Which of the following is a common method for ensuring data quality during integration?
     A) Data cleansing
     B) Data validation
     C) Regular audits
     D) All of the above
     Answer: D) All of the above

113. What is the purpose of using APIs in data integration?
     A) To facilitate communication between different software applications
     B) To visualize data
     C) To clean data
     D) To collect more data
     Answer: A) To facilitate communication between different software applications

114. Which of the following is a potential drawback of data integration?
     A) Increased complexity
     B) Higher costs
     C) Potential data loss
     D) All of the above
     Answer: D) All of the above

115. What is the role of data governance in data transformation?
     A) To establish policies for data quality and usage
     B) To visualize data
     C) To collect more data
     D) To analyze data
     Answer: A) To establish policies for data quality and usage

116. Which of the following is a common technique for data compression?
     A) Lossless compression
     B) Lossy compression
     C) Both A and B
     D) None of the above
     Answer: C) Both A and B

117. What is the significance of using a data warehouse in data integration?
     A) To provide a centralized repository for integrated data
     B) To visualize data
     C) To clean data
     D) To collect more data
     Answer: A) To provide a centralized repository for integrated data

118. Which of the following is a method for ensuring data accuracy during integration?
     A) Cross-validation with external datasets
     B) Implementing automated checks
     C) Manual verification
     D) All of the above
     Answer: D) All of the above

119. What is the purpose of using data visualization in the context of data integration?
     A) To help stakeholders understand complex data relationships
     B) To clean data
     C) To collect more data
     D) To analyze data
     Answer: A) To help stakeholders understand complex data relationships

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